1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
29/3/2025 Comida 70591 Tami Supermercado
3/4/2025 Gas 83300 Andrés NA
4/4/2025 Agua 20807 Andrés NA
6/4/2025 Comida 52655 Tami Supermercado
12/4/2025 Comida 72108 Tami Supermercado
16/4/2025 VTR 21990 Andrés NA
22/4/2025 Comida 107881 Tami Supermercado
26/4/2025 Comida 55874 Tami Supermercado
28/4/2025 Comida 13050 Tami Cervezas MUT
29/4/2025 Electricidad 52507 Andrés enel
29/4/2025 Diosi 11990 Andrés arena 7kg superzoo
3/5/2025 Agua 17072 Andrés aguas andina
13/5/2025 VTR 22000 Andrés NA
17/5/2025 Electricidad 52404 Andrés NA
13/6/2025 VTR 22000 Andrés NA
22/6/2025 Electricidad 52401 Andrés NA
27/7/2025 Electricidad 52000 Andrés NA
27/7/2025 Comida 59147 Tami Supermercado
29/7/2025 Comida 10000 Andrés complemento comida
29/7/2025 Electrodomésticos/mantención casa 68000 Andrés NA
30/7/2025 Comida 24140 Tami Supermercado
31/7/2025 Gas 19100 Andrés NA
3/8/2025 Comida 86089 Tami Supermercado
10/8/2025 Comida 108649 Tami Supermercado
12/8/2025 Enceres 13490 Tami Confort
16/8/2025 VTR 22000 Andrés NA
17/8/2025 Comida 72586 Tami Supermercado
20/8/2025 Electricidad 65242 Andrés 49393- 42306 -47872= 1521
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 1.0200e+09   2    5.2713 0.0053 ** 
## lag_depvar    2.6335e+11   1 2721.9292 <2e-16 ***
## Residuals     8.2142e+10 849                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1766.028 16223.70 0.1430829
## 2-0 31328.843 23238.654 39419.03 0.0000000
## 2-1 24100.005 19421.810 28778.20 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
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## 29   28706.00             0   28640.00
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## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 819  29568.00             2   36772.29
## 820  42362.00             2   29568.00
## 821  42566.29             2   42362.00
## 822  39596.00             2   42566.29
## 823  32925.00             2   39596.00
## 824  43416.57             2   32925.00
## 825  52624.86             2   43416.57
## 826  57733.71             2   52624.86
## 827  54120.57             2   57733.71
## 828  53353.43             2   54120.57
## 829  56286.86             2   53353.43
## 830  60626.86             2   56286.86
## 831  61375.29             2   60626.86
## 832  53710.86             2   61375.29
## 833  55795.57             2   53710.86
## 834  55130.14             2   55795.57
## 835  57700.14             2   55130.14
## 836  61333.14             2   57700.14
## 837  59230.71             2   61333.14
## 838  49195.00             2   59230.71
## 839  55436.43             2   49195.00
## 840  50353.14             2   55436.43
## 841  43194.86             2   50353.14
## 842  47539.71             2   43194.86
## 843  35271.00             2   47539.71
## 844  34774.86             2   35271.00
## 845  48788.71             2   34774.86
## 846  50717.71             2   48788.71
## 847  51727.43             2   50717.71
## 848  51313.14             2   51727.43
## 849  56125.29             2   51313.14
## 850  68503.71             2   56125.29
## 851  62945.00             2   68503.71
## 852  50209.71             2   62945.00
## 853  49436.29             2   50209.71
## 854  55308.00             2   49436.29
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   697 53563.10 21907.996
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29  29568.00  42362.00
## [820]  42566.29  39596.00  32925.00  43416.57  52624.86  57733.71  54120.57
## [827]  53353.43  56286.86  60626.86  61375.29  53710.86  55795.57  55130.14
## [834]  57700.14  61333.14  59230.71  49195.00  55436.43  50353.14  43194.86
## [841]  47539.71  35271.00  34774.86  48788.71  50717.71  51727.43  51313.14
## [848]  56125.29  68503.71  62945.00  50209.71  49436.29  55308.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [852] 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2014.442788   4038.467928   -536.146901   2439.740343  -2965.825240 
##             7             8             9            10            11 
##    520.129605  -5653.814103  -1189.994362  -3968.548863   -422.697055 
##            12            13            14            15            16 
##  -4943.757909  -1616.292282   -906.573975    371.262926  -3247.568905 
##            17            18            19            20            21 
##   -384.040300  -2135.310210   6598.400141  -1528.908789  -1208.827462 
##            22            23            24            25            26 
##   1474.613892  -1185.976257    234.684364   1695.619965  -7099.798036 
##            27            28            29            30            31 
##    944.635627   8191.100197    423.877766     -8.175543  -2394.975463 
##            32            33            34            35            36 
##   1579.802704   4577.385388   1135.217010   2400.116865  -1858.035809 
##            37            38            39            40            41 
##   4615.769955   4308.382896  -2268.022139  -2976.382405  -1107.936158 
##            42            43            44            45            46 
## -10740.314511   7282.218352   2556.571581   1368.222286   8107.445860 
##            47            48            49            50            51 
##    694.356055   6536.507974   6726.190729  -5866.855458  -4786.309432 
##            52            53            54            55            56 
##  -5055.324424  -7928.495356   6123.913634  -4077.081264  -4897.795968 
##            57            58            59            60            61 
##   3849.088689    884.898534    -33.989940    140.583923  -4997.735631 
##            62            63            64            65            66 
##  18121.882744   3650.161482  -3634.975143   5932.428264   7354.836219 
##            67            68            69            70            71 
##  14654.051783   1717.765402 -13189.764204  -1295.843032   4651.702430 
##            72            73            74            75            76 
##  -4889.472952  -4398.606589 -10495.380003   2461.154236  -5402.640169 
##            77            78            79            80            81 
##   1057.507946  -6870.745542    538.182243  -2361.617562  -2701.548032 
##            82            83            84            85            86 
##  -3940.356843   -547.588038   2305.598787   3756.580583    474.321478 
##            87            88            89            90            91 
##   -486.456482    194.597744   4300.330530  -1161.296520   1151.537711 
##            92            93            94            95            96 
##  -2063.005357  -1044.460599    176.729480    274.207888  -7484.301624 
##            97            98            99           100           101 
##   2386.298190  -8605.436999  -2949.007695  -4048.600024  -1747.213924 
##           102           103           104           105           106 
##  -1271.384096   3171.587603  -2347.837919   2587.456609  -1162.218691 
##           107           108           109           110           111 
##    966.414281   2584.323699  -3154.926190  -4725.647538   -855.713786 
##           112           113           114           115           116 
##   1898.295314  11690.419647  -1237.065245   2672.562079   4268.339926 
##           117           118           119           120           121 
##   3510.604119  -1090.414302  -4708.632890  -3720.527683   2320.434283 
##           122           123           124           125           126 
##  -1730.290540   1341.418890   8860.200244    855.175865    138.238361 
##           127           128           129           130           131 
##  -2514.229116   2659.581548   7058.469258   1022.732473  -8489.572835 
##           132           133           134           135           136 
##   1751.919030   4139.220514  -3157.706521  -1416.397648   -851.955045 
##           137           138           139           140           141 
##  -3878.728692   1181.558932   -495.832355  -2914.154291   1715.747511 
##           142           143           144           145           146 
##  -1881.889916  -7831.234922   2032.382862  -3484.390136   2095.763118 
##           147           148           149           150           151 
##   -261.625924   1019.243689   -361.872447   1349.719562   1185.438404 
##           152           153           154           155           156 
##   3356.389402  -4859.576499  -1175.882298  -3237.805027   5952.776374 
##           157           158           159           160           161 
##   9747.403320  -3660.466534  -5008.022838   3372.484374    -32.679997 
##           162           163           164           165           166 
##   2469.790973  -6134.573343  -6974.924075   3925.808886  17165.042193 
##           167           168           169           170           171 
##   3403.871021   -622.501482  -2671.161300  -1330.986360   3362.625128 
##           172           173           174           175           176 
##   -455.646116  -8303.694932   2632.201790   4094.496843    395.306634 
##           177           178           179           180           181 
##   8519.706690  -9477.590937  -3704.261347 -10978.516671 -11477.253598 
##           182           183           184           185           186 
##    994.864804   9054.201125  -1666.562395   5691.416459   6318.277980 
##           187           188           189           190           191 
##  12919.955924   8190.186570  -4307.693055   2215.661622  10115.903304 
##           192           193           194           195           196 
##  -1899.896483  -2703.807674 -10541.891913  -6625.367837    971.715641 
##           197           198           199           200           201 
##  -5494.672084 -10056.314437   5125.242979  -3325.191512  -1967.971228 
##           202           203           204           205           206 
##  -1058.561351   6240.638609   9625.988537    317.551199   2662.175053 
##           207           208           209           210           211 
##   2833.725926   5518.680435  12566.113553  -5958.319534 -11565.681070 
##           212           213           214           215           216 
##  -5932.520022 -10850.947167  -5334.704884   1270.221198 -13265.906940 
##           217           218           219           220           221 
##  16138.535985   7554.448227   1270.031885  26428.759206  12255.308480 
##           222           223           224           225           226 
##   7057.484923  13745.337118  -4200.634268  -2026.562106   3493.202505 
##           227           228           229           230           231 
##     76.187073   2464.551689   8725.176389   5552.432265  -2180.960874 
##           232           233           234           235           236 
##  -2099.282477   9157.049352 -11778.114029  -7549.122877  -8804.210003 
##           237           238           239           240           241 
## -10361.626524   2820.845292   1095.488296  -8553.227551  -9242.599363 
##           242           243           244           245           246 
##   8844.990365  -8017.634353   2237.652222 -10551.636406  -4301.122388 
##           247           248           249           250           251 
##   1176.601698    755.864554 -12564.214580   3398.790201   1815.512585 
##           252           253           254           255           256 
##   3962.717935   1882.468320  -1415.233047  10883.628820  20617.687086 
##           257           258           259           260           261 
##   2918.236994  -4554.343393   3827.659157  -1979.514291   3453.078620 
##           262           263           264           265           266 
##  -5137.993799 -11176.627201  -5004.625441   -794.417069  -5460.426396 
##           267           268           269           270           271 
##   8509.025844  -4556.632120   3915.127966  -2385.362893   4153.045198 
##           272           273           274           275           276 
##    426.739043   7019.167880  -1703.151583  11734.190730  -4887.972478 
##           277           278           279           280           281 
##   1424.382165   -675.393664   7548.644544  -5367.985692  -3035.285343 
##           282           283           284           285           286 
## -11560.714950  -2953.949210  18374.585428   7482.077581   2423.681495 
##           287           288           289           290           291 
##   -941.696781    594.603419   6086.745638   6564.080955 -19097.762983 
##           292           293           294           295           296 
## -11434.978299  -8398.071798   9402.809483   2797.983643  -1456.383901 
##           297           298           299           300           301 
##  27127.333926   9749.884846   4571.962512   9184.663063   2512.541208 
##           302           303           304           305           306 
##  -1375.997568   7559.651611 -24639.316370  -3831.601118   -461.295631 
##           307           308           309           310           311 
##  -7249.812782  -4237.732832   2676.186550  -9450.949099  -3469.987340 
##           312           313           314           315           316 
##  -8418.335150   1349.509946  -3370.160592   1834.060788  -4302.650662 
##           317           318           319           320           321 
##  27230.273873  -1013.613313   3002.642820  10535.634520   5274.618449 
##           322           323           324           325           326 
##  32058.269471   4732.988311 -21313.536392   1467.309183    788.393806 
##           327           328           329           330           331 
##  -6782.942948  -2033.214443 -33558.109581    698.885618  -2483.584875 
##           332           333           334           335           336 
##   -267.165666  -3340.446769   3920.085403   -612.599377  -7127.923213 
##           337           338           339           340           341 
##  -3277.284138  -2347.115908  -7832.287138   3713.578452  -1522.834438 
##           342           343           344           345           346 
##  -1890.035963  -1145.778022     23.303347    324.022788  -1781.304413 
##           347           348           349           350           351 
##  -9609.665393 -13355.422626   2190.120464  -4455.624680  -3786.510007 
##           352           353           354           355           356 
##  -6105.347101   1633.117154   1254.371955   2611.742945  -3922.702098 
##           357           358           359           360           361 
##   -669.922465    518.440687   6846.801073     85.654011   -233.792616 
##           362           363           364           365           366 
##   2384.359817  -2958.395742  -1078.970513  -8943.230888  -4801.210265 
##           367           368           369           370           371 
##  -6375.854252  -5097.642493  -7390.325768   4892.716346    226.550397 
##           372           373           374           375           376 
##   6967.769144  -7814.723359  -2422.303028  -3544.182709  -2617.092779 
##           377           378           379           380           381 
## -12604.393607   1786.979311 -10762.923453   5590.998101   9204.713017 
##           382           383           384           385           386 
##   2958.484634  -2581.715310   1424.033321   6553.932001  11195.517683 
##           387           388           389           390           391 
##  -6056.552221  -5603.873176   -385.463300   8333.554404   1556.855593 
##           392           393           394           395           396 
##  10957.314145 -10178.405311   2504.139238    434.472130    283.284251 
##           397           398           399           400           401 
##   -932.980004   -838.356753 -14758.833172   8304.469088  -1421.162813 
##           402           403           404           405           406 
##  -1605.513144   6755.740807  -8179.494114  -1514.268331  -2741.336403 
##           407           408           409           410           411 
##  -6017.418427  -3035.381834  -4083.100903  -8908.789643   6006.700240 
##           412           413           414           415           416 
##   1487.805352  -7536.086626  -7833.894164  14100.148405   3637.263472 
##           417           418           419           420           421 
##   4292.509875  -8255.970880  -4937.833094  -2778.660713   2650.426873 
##           422           423           424           425           426 
## -14191.852250  -2926.639021  -9228.405947   2909.137986   6855.318743 
##           427           428           429           430           431 
##   6421.787128  -4169.845449  -4292.587451  -4882.706318  -1937.386986 
##           432           433           434           435           436 
##  -5857.772776  -6758.014735  -6064.708455  -1495.807359   -953.990118 
##           437           438           439           440           441 
##  -5086.757671   2478.371230   4718.568984  -5201.805356  -2292.307969 
##           442           443           444           445           446 
##   1443.267532  -3981.700380   2698.349518  -6730.518138 -12245.863104 
##           447           448           449           450           451 
##  -4613.329534   9549.930323  -2166.090744   4621.645901  -6022.539946 
##           452           453           454           455           456 
##  -1260.485683    245.166884   2882.009219 -12426.269304   3246.033884 
##           457           458           459           460           461 
##  -6840.358536   6399.197463   2863.482200   2345.130263  -4018.175986 
##           462           463           464           465           466 
##   1930.432499   -178.517340   1620.045536   -701.301487   3170.856940 
##           467           468           469           470           471 
##  -2830.068508   5622.948005  -7141.827154  -3139.971482  -2370.608713 
##           472           473           474           475           476 
##  -4821.859199   2852.181436   7641.842479  -6197.941717   1322.306715 
##           477           478           479           480           481 
##  -6345.831904  -2992.868620   1870.502245 -13079.663967  -9869.628296 
##           482           483           484           485           486 
##  -1294.242300    -75.537901  -1065.214474  -1448.762884  -9694.164330 
##           487           488           489           490           491 
##  11006.590262   6106.886497   7269.143016  -5612.177343   5206.689268 
##           492           493           494           495           496 
##   9114.940171   5848.186783 -13695.121349 -10744.411204  -3587.757951 
##           497           498           499           500           501 
##  -1243.674141   -661.007626  -7763.360084    493.571791   4164.926871 
##           502           503           504           505           506 
##   5370.231315    503.754481    -79.007169  -7399.516947    429.549071 
##           507           508           509           510           511 
##  -5192.218095   1701.207782  -1435.644660  -8295.638877   -719.560620 
##           512           513           514           515           516 
##  -2794.388803   -704.216694   1212.971241  -9622.364140  -7870.063409 
##           517           518           519           520           521 
##  24197.019262   9661.085567   5686.173593  -5544.586018   2606.582156 
##           522           523           524           525           526 
##  16820.456761  11228.206666 -24421.849228  -5256.864538  -3913.671177 
##           527           528           529           530           531 
##   4403.396563   -538.607036 -11282.893916   4243.349263  13747.516474 
##           532           533           534           535           536 
##  -5179.279501   4186.762378   5355.912319  -2004.066275  -4746.809404 
##           537           538           539           540           541 
##  -7264.266826  -2268.435665   8161.304697    -57.664572  -8325.349692 
##           542           543           544           545           546 
##   1656.556907   -764.415382    203.118732 -11195.230923 -11195.083568 
##           547           548           549           550           551 
##   1935.606529   6887.629276  -1457.414393    698.376620  -7864.095591 
##           552           553           554           555           556 
##   8440.996722    755.025587 -12100.285711   9037.793290   8502.665739 
##           557           558           559           560           561 
##    -83.250839   4670.065804  -3771.438918  13922.382447  21272.398661 
##           562           563           564           565           566 
##  -6751.538605  -9939.585620   6550.191579    -13.974716   3219.128757 
##           567           568           569           570           571 
##  -7617.346630 -17520.394485   6505.854579   6260.707109   1712.537029 
##           572           573           574           575           576 
##   2906.356808   1572.580545  -2363.710070  14527.993025  -9877.523991 
##           577           578           579           580           581 
##  -6441.951438   8533.910673   2657.890633  -6751.055982   7325.153914 
##           582           583           584           585           586 
##  -4002.516686  -2964.163994  15524.361239 -14719.762577   8251.757112 
##           587           588           589           590           591 
##   -127.786286  -6407.204651   -928.022738     82.467604 -10821.575511 
##           592           593           594           595           596 
##   1659.592441  -7287.259542   2943.994128   8732.200961  -7660.777263 
##           597           598           599           600           601 
##   5711.751624   2577.731225   6690.861641  -3373.321420   5977.152969 
##           602           603           604           605           606 
##  -8485.995566   2093.203867   1102.877741   2969.225161   1319.382858 
##           607           608           609           610           611 
##    219.915775  -5986.510559   7916.946154  -1361.800642  -2745.890374 
##           612           613           614           615           616 
##  -3615.286437  -8378.782226  11831.107268   4776.882893  -9485.260390 
##           617           618           619           620           621 
##  11482.384753   5876.448892  -5758.905158  26192.950479 -13053.846308 
##           622           623           624           625           626 
##  -6965.294190   2994.272343  -4326.911718 -10746.203710  11170.709264 
##           627           628           629           630           631 
## -21789.295282  -2515.664390   8577.317657  11013.981085  -1701.672483 
##           632           633           634           635           636 
##  33140.132737  -6808.235366   5519.728606   5194.420715  -2479.022046 
##           637           638           639           640           641 
##  -5542.676200  -2118.601668 -12600.378356  -2379.513516  -2017.540697 
##           642           643           644           645           646 
##  -2647.127454  -2979.317806   1699.458388   4309.738434  16832.484567 
##           647           648           649           650           651 
##  18381.510527    671.848932   4581.545520  10399.544765  19921.747636 
##           652           653           654           655           656 
##    484.207664 -28310.036981  -1512.878520  -2452.468315   1718.682590 
##           657           658           659           660           661 
##  -3339.571044 -10757.285572   1554.313631   4113.958146  -1126.250278 
##           662           663           664           665           666 
##  12916.262548   1220.631751   1674.294614 -11830.504933   1264.198994 
##           667           668           669           670           671 
##   1071.259838  -5282.509605  -7515.333047   1975.855179  -3806.140472 
##           672           673           674           675           676 
##   2585.196268  -3472.585475  -9425.218544  -8381.797423  -3045.510600 
##           677           678           679           680           681 
##    103.841121   2773.170737    631.272911  -3912.226807  -1890.812192 
##           682           683           684           685           686 
##  -1400.671662  -8325.625247   4574.386601  -2326.897891  -1482.043307 
##           687           688           689           690           691 
##    503.142579  10766.156535   9745.455832  10506.847966  -9790.182213 
##           692           693           694           695           696 
##  -3662.810763  -3241.591387   5774.385219 -10488.917071  -7999.863868 
##           697           698           699           700           701 
##  -8690.017558  -6344.743946  -4805.621980   3017.147073  -4474.228557 
##           702           703           704           705           706 
##  -1968.751074   4150.531751  31027.939447   9433.811825  23365.138298 
##           707           708           709           710           711 
##   1614.082372   8263.692777  22869.402646   6525.278148 -18230.276373 
##           712           713           714           715           716 
##   4793.111891  -5469.151408   -128.790955    450.628753 -17296.588793 
##           717           718           719           720           721 
##  -5303.306228   3293.249930  -3053.198997 -13019.369990   4233.196249 
##           722           723           724           725           726 
##  -5599.992684    696.656464  -3979.501604 -12493.296452   1316.693163 
##           727           728           729           730           731 
##  -1916.134357  -9826.454562  17216.611156   1731.846290  -2765.092069 
##           732           733           734           735           736 
##   5671.886533  -8670.973005   -767.559257   8094.322946 -15391.444285 
##           737           738           739           740           741 
##  -5957.771666   7359.469032  -4829.079652    114.537967   1782.088098 
##           742           743           744           745           746 
##  -2000.164718  -5212.841374   6366.004064  -6318.098314  22653.102771 
##           747           748           749           750           751 
##   7792.893562  -1977.869552  -7320.875633  23379.675310  -4315.569049 
##           752           753           754           755           756 
##   1368.991949 -14449.427474  56073.321869  26921.808847  15114.126560 
##           757           758           759           760           761 
## -10618.868239  10623.943480   7335.665305   5831.923242 -46366.626045 
##           762           763           764           765           766 
## -16183.879114    941.698115  -2540.832695  -3482.338686 122817.747609 
##           767           768           769           770           771 
##  19389.205956  43805.398506  22698.457797  12194.795193  15961.214290 
##           772           773           774           775           776 
##  25840.328008 -98688.292172  -6739.476052 -35823.724257   1711.047430 
##           777           778           779           780           781 
##  -1251.726712   3373.393927  -7433.105802  -1468.988447  -1968.936124 
##           782           783           784           785           786 
##   3400.632694  -7174.224820  -2260.943587   3895.113478   2246.509056 
##           787           788           789           790           791 
##  -2774.419813  -4064.104708   1719.868883   2837.532868    -63.454776 
##           792           793           794           795           796 
##  -6714.644487  -5799.051634  -1167.841869  -1283.017506  -7836.733713 
##           797           798           799           800           801 
##  -2378.947815  -3293.216653  -2691.439662  10698.251399   2227.081551 
##           802           803           804           805           806 
##   7068.510643   2919.286692  -5449.920788   8180.663229   9875.419053 
##           807           808           809           810           811 
## -10593.208073  -7398.100737  -7513.396881   2991.765334   4184.042422 
##           812           813           814           815           816 
##  -2275.325136 -14172.316960  -4129.580428   6238.861257   8223.859504 
##           817           818           819           820           821 
##  -9678.071092  -7762.780100  -9354.266709   9729.810154  -1236.372181 
##           822           823           824           825           826 
##  -4385.020178  -8462.657534   7853.377890   7901.450736   4970.534441 
##           827           828           829           830           831 
##  -3103.162229   -715.662531   2887.560067   4666.377471   1625.543002 
##           832           833           834           835           836 
##  -6692.340114   2084.202722   -401.394235   2749.592837   4138.720484 
##           837           838           839           840           841 
##  -1135.687955  -9335.767476   5667.864143  -4864.825858  -7584.884227 
##           842           843           844           845           846 
##   3009.887084 -13052.331023  -2836.633882  11610.406623   1303.878761 
##           847           848           849           850           851 
##    629.379128   -666.490232   4507.366345  12684.302964  -3682.043091 
##           852           853           854 
## -11564.003784  -1218.227871   5328.768512 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17254.84  20100.53  24352.29  24070.40  26422.54  23756.58  24472.53  19707.14 
##        10        11        12        13        14        15        16        17 
##  19443.83  16787.98  17565.04  14296.15  14347.29  15011.59  16707.28  15028.18 
##        18        19        20        21        22        23        24        25 
##  16062.31  15436.17  22514.91  21599.40  21079.53  22968.55  22294.89  22947.09 
##        26        27        28        29        30        31        32        33 
##  24792.08  18723.65  20448.90  28282.12  28339.75  28012.83  25643.48  27045.19 
##        34        35        36        37        38        39        40        41 
##  30886.21  31234.45  32642.89  30154.80  34134.62  37341.02  34398.67  31211.22 
##        42        43        44        45        46        47        48        49 
##  30059.60  20644.07  28158.86  30594.06  31682.70  38517.22  38012.06  42671.81 
##        50        51        52        53        54        55        56        57 
##  46905.86  39607.60  34178.90  29204.21  22352.23  28638.94  25221.37  21520.91 
##        58        59        60        61        62        63        64        65 
##  25926.96  27185.85  27482.70  27894.31  23767.40  40349.98  42192.98  37441.43 
##        66        67        68        69        70        71        72        73 
##  41646.16  46559.23  57221.81  55236.62  40487.56  37994.73  41011.04  35314.18 
##        74        75        76        77        78        79        80        81 
##  30768.81  21477.13  24676.93  20604.78  22689.75  17587.96  19602.33  18829.26 
##        82        83        84        85        86        87        88        89 
##  17857.50  15927.45  17204.54  20810.71  25226.11  26215.46  26240.40  26856.81 
##        90        91        92        93        94        95        96        97 
##  30979.73  29810.89  30809.72  28875.17  28075.41  28443.36  28849.73  22430.56 
##        98        99       100       101       102       103       104       105 
##  25444.01  18478.15  17334.89  15376.64  15676.24  16353.27  20823.55  19907.54 
##       106       107       108       109       110       111       112       113 
##  23416.79  23206.87  24882.10  27757.35  25256.79  21702.14  21977.42  24622.29 
##       114       115       116       117       118       119       120       121 
##  35481.07  33674.87  35511.37  38508.11  40462.99  38152.63  32976.38  29319.71 
##       122       123       124       125       126       127       128       129 
##  31401.43  29682.30  30863.23  38458.97  38101.62  37163.66  34028.85  35809.10 
##       130       131       132       133       134       135       136       137 
##  41204.12  40644.72  31851.08  33115.21  36303.28  32715.83  31103.96  30189.44 
##       138       139       140       141       142       143       144       145 
##  26748.30  28161.98  27931.73  25619.25  27642.60  26268.09  19873.62  22902.53 
##       146       147       148       149       150       151       152       153 
##  20730.38  23705.91  24245.61  25835.16  26017.14  27670.42  28970.47  32001.01 
##       154       155       156       157       158       159       160       161 
##  27473.60  26736.95  24293.51  30184.45  41680.90  40012.02  37378.37  42395.97 
##       162       163       164       165       166       167       168       169 
##  43803.78  47217.86  42686.21  37995.91  43418.24  59711.70  61922.64  60337.59 
##       170       171       172       173       174       175       176       177 
##  57164.99  55565.09  58266.22  57290.84  49587.08  52409.07  56149.69  56185.86 
##       178       179       180       181       182       183       184       185 
##  63310.88  53818.26  50570.95  41384.54  32928.42  36434.80  46532.85  45989.15 
##       186       187       188       189       190       191       192       193 
##  51938.72  57680.62  68457.81  73737.84  67435.91  67629.24  74695.75  70374.52 
##       194       195       196       197       198       199       200       201 
##  65899.75  55149.37  49182.71  50606.24  46203.31  38376.33  44797.62  43025.97 
##       202       203       204       205       206       207       208       209 
##  42664.13  43142.22  49932.58  58817.02  58446.82  60170.70  61825.61  65614.74 
##       210       211       212       213       214       215       216       217 
##  75076.18  67163.25  55358.66  49970.38  40971.56  37930.92  41042.91  31068.46 
##       218       219       220       221       222       223       224       225 
##  48032.84  55349.68  56251.10  79004.26  86495.23  88497.38  96084.63  87040.42 
##       226       227       228       229       230       231       232       233 
##  81042.08  80624.24  77276.02  76437.97  81172.42  82535.96  76974.43  72189.95 
##       234       235       236       237       238       239       240       241 
##  77840.54  64495.55  56536.35  48491.34  40107.44  44297.08  46448.66  39902.89 
##       242       243       244       245       246       247       248       249 
##  33585.87  43862.78  38112.78  42046.35  34314.41  33020.97  36674.28  39496.64 
##       250       251       252       253       254       255       256       257 
##  30331.07  36265.92  40065.28  45257.25  47974.09  47466.94  57762.31  75250.05 
##       258       259       260       261       262       263       264       265 
##  75065.20  68379.48  69860.51  66083.35  67528.71  61289.77  50570.20  46599.70 
##       266       267       268       269       270       271       272       273 
##  46809.00  42917.83  51717.20  47992.30  52136.79  50254.38  54319.55  54615.40 
##       274       275       276       277       278       279       280       281 
##  60629.58  58265.09  67932.83  61860.90  62070.82  60420.78  66160.56  59894.43 
##       282       283       284       285       286       287       288       289 
##  56460.14  46018.09  44415.70  61638.64  67165.75  67574.98  64993.97  64081.83 
##       290       291       292       293       294       295       296       297 
##  68080.63  71988.76  52995.55  43102.93  37117.19  47433.02  50673.10  49787.52 
##       298       299       300       301       302       303       304       305 
##  73970.83  79913.04  80580.34  85190.32  83389.85  78422.78  81887.74  56800.03 
##       306       307       308       309       310       311       312       313 
##  53063.15  52743.10  46536.59  43747.53  47348.95  39905.13  38627.91  33192.35 
##       314       315       316       317       318       319       320       321 
##  36974.87  36156.65  39986.08  37971.58  63744.18  61586.50  63209.22  71203.10 
##       322       323       324       325       326       327       328       329 
##  73589.16  99057.30  97435.82  73278.83  72077.32  70435.51  62391.50  59515.25 
##       330       331       332       333       334       335       336       337 
##  29479.54  33165.16  33604.45  35923.16  35264.34  41028.31  42103.35  37353.43 
##       338       339       340       341       342       343       344       345 
##  36568.26  36694.86  32016.28  38012.12  38675.18  38933.49  39808.84  41593.83 
##       346       347       348       349       350       351       352       353 
##  43414.88  43166.67  36114.99  26687.74  32029.62  30891.22  30481.49  28099.17 
##       354       355       356       357       358       359       360       361 
##  32775.63  36527.97  40989.27  39179.21  40438.85  42576.20  49967.63  50517.94 
##       362       363       364       365       366       367       368       369 
##  50719.50  53181.40  50666.11  50110.95  42759.92  39958.14  36137.07  33916.90 
##       370       371       372       373       374       375       376       377 
##  29976.71  37260.88  39546.66  47428.15  41402.87  40850.33  39388.38  38921.39 
##       378       379       380       381       382       383       384       385 
##  29793.73  34389.49  27444.72  35659.86  45987.66  49551.29  47825.54  49816.21 
##       386       387       388       389       390       391       392       393 
##  56033.20  65513.84  58728.59  53199.61  52928.45  60304.29  60827.40  69491.69 
##       394       395       396       397       398       399       400       401 
##  58602.86  60168.96  59729.29  59213.41  57701.07  56463.26  43228.53  51809.88 
##       402       403       404       405       406       407       408       409 
##  50810.80  49777.54  56175.64  48721.84  48033.34  46360.85  42040.24  40871.53 
##       410       411       412       413       414       415       416       417 
##  38936.36  33033.44  40902.34  43827.23  38502.18  33592.85  48457.17  52300.06 
##       418       419       420       421       422       423       424       425 
##  56227.40  48700.26  45025.37  43702.00  47286.71  35711.50  35440.83  29702.43 
##       426       427       428       429       430       431       432       433 
##  35289.54  43613.07  50501.85  47268.87  44338.99  41265.67  41153.92  37633.44 
##       434       435       436       437       438       439       440       441 
##  33773.71  31009.09  32584.42  34432.90  32438.49  37302.29  43504.81  40258.74 
##       442       443       444       445       446       447       448       449 
##  39964.88  42969.84  40856.94  44844.52  40093.72  31130.33  29968.36  41319.81 
##       450       451       452       453       454       455       456       457 
##  41001.50  46649.97  42288.20  42637.69  44257.42  47973.84  37852.97  42699.93 
##       458       459       460       461       462       463       464       465 
##  38125.37  45690.80  49209.16  51828.46  48559.57  50899.23  51100.67  52846.87 
##       466       467       468       469       470       471       472       473 
##  52344.71  55287.07  52616.62  57665.40  50928.54  48540.61  47127.43  43753.39 
##       474       475       476       477       478       479       480       481 
##  47507.73  54967.51  49397.12  51099.55  45890.87  44270.64  47102.24  36521.49 
##       482       483       484       485       486       487       488       489 
##  30086.10  31954.54  34649.93  36139.19  37104.59  30748.41  43272.68  49929.71 
##       490       491       492       493       494       495       496       497 
##  56756.75  51470.74  56301.49  63931.53  67741.12  54003.98  44586.33  42612.25 
##       498       499       500       501       502       503       504       505 
##  42935.29  43726.07  38215.43  40613.22  45912.20  51591.10  52300.44  52410.95 
##       506       507       508       509       510       511       512       513 
##  46115.88  47455.22  43716.22  46470.36  46136.21  39854.99  40985.53  40161.07 
##       514       515       516       517       518       519       520       521 
##  41266.17  43904.94  36748.49  32030.12  55908.34  64065.11  67716.30  61098.56 
##       522       523       524       525       526       527       528       529 
##  62437.40  76016.51  82989.85  57952.15  52824.67  49520.60  53897.46  53404.04 
##       530       531       532       533       534       535       536       537 
##  43592.37  48581.77  61236.14  55759.67  59155.66  63141.49  60195.52  55228.70 
##       538       539       540       541       542       543       544       545 
##  48694.15  47350.70  55283.95  55034.49  47598.16  49820.70  49647.45  50340.95 
##       546       547       548       549       550       551       552       553 
##  40994.51  32834.25  37173.94  45286.56  45083.62  46788.67  40801.43  49809.97 
##       554       555       556       557       558       559       560       561 
##  50964.71  40748.92  50285.19  58144.11  57509.36  61105.30  56874.62  68629.32 
##       562       563       564       565       566       567       568       569 
##  85309.68  75405.59  63974.81  68391.83  66517.16  67703.20  59277.39  43274.43 
##       570       571       572       573       574       575       576       577 
##  50279.58  56181.75  57363.93  59438.42  60085.14  57213.01  69453.52  58832.24 
##       578       579       580       581       582       583       584       585 
##  52558.38  60156.11  61659.34  54756.85  61020.23  56598.59  53644.64  67207.91 
##       586       587       588       589       590       591       592       593 
##  52643.81  59984.36  59077.20  52802.59  52108.10  52384.00  43104.55  45899.97 
##       594       595       596       597       598       599       600       601 
##  40529.15  44772.80  53531.63  46866.25  52722.27  55098.85  60765.04  56925.13 
##       602       603       604       605       606       607       608       609 
##  61736.42  53309.37  55188.41  55964.35  58271.33  58845.08  58386.08  52566.48 
##       610       611       612       613       614       615       616       617 
##  59624.51  57685.60  54784.29  51492.07  44458.61  55962.97  59848.40  50788.47 
##       618       619       620       621       622       623       624       625 
##  61185.12  65367.91  58861.05  81077.13  66207.58  58540.87  60542.77  55898.49 
##       626       627       628       629       630       631       632       633 
##  46238.86  56940.72  37507.09  37367.40  46930.73  57407.96  55453.58  84167.66 
##       634       635       636       637       638       639       640       641 
##  74358.99  76558.58  78195.02  72924.10  65647.17  62283.24  50194.51  48563.68 
##       642       643       644       645       646       647       648       649 
##  47455.84  45938.89  44324.40  46999.83  51614.80  66577.78  80994.44  78119.31 
##       650       651       652       653       654       655       656       657 
##  79022.60  84890.97  98328.51  93089.89  63375.74  60828.90  57784.89  58769.00 
##       658       659       660       661       662       663       664       665 
##  55211.86  45629.69  48012.76  52328.25  51520.88  63076.51  62954.28  63243.65 
##       666       667       668       669       670       671       672       673 
##  51705.23  53064.03  54081.94  49423.19  43406.14  46439.43  44039.52  47524.44 
##       674       675       676       677       678       679       680       681 
##  45278.08  38119.51  32780.37  32777.87  35525.40  40254.87  42514.08  40519.67 
##       682       683       684       685       686       687       688       689 
##  40543.24  40991.77  35337.18  41663.18  41160.90  41460.00  43454.41  54156.40 
##       690       691       692       693       694       695       696       697 
##  62609.15  70654.04  59956.67  55966.59  52850.61  58001.92  48300.01  42002.45 
##       698       699       700       701       702       703       704       705 
##  35901.46  32622.34  31103.14  36606.80  34871.32  35543.61  41473.35  70117.33 
##       706       707       708       709       710       711       712       713 
##  76272.58  93810.20  90131.45  92725.31 107742.29 106583.56  83957.75  84304.87 
##       714       715       716       717       718       719       720       721 
##  75647.93  72752.23  70729.87  53469.02  48869.89  52360.06  49866.23  38987.38 
##       722       723       724       725       726       727       728       729 
##  44552.28  40825.63  43069.50  40945.87  31658.31  35606.85  36231.74  29870.82 
##       730       731       732       733       734       735       736       737 
##  47928.44  50174.81  48209.83  53860.54  46271.42  46545.82  54522.73  40981.91 
##       738       739       740       741       742       743       744       745 
##  37395.96  45892.37  42668.75  44170.48  46937.59  46051.27  42472.42  49457.24 
##       746       747       748       749       750       751       752       753 
##  44481.18  65431.39  70748.58  66860.16  58800.18  78567.71  71646.01  70565.86 
##       754       755       756       757       758       759       760       761 
##  55811.68 104503.33 121563.87 126150.15 107686.91 110113.76 109361.65 107392.05 
##       762       763       764       765       766       767       768       769 
##  60097.74  45157.59  47065.69  45691.05  43668.82 152176.08 156610.32 181799.69 
##       770       771       772       773       774       775       776       777 
## 185364.06 179305.36 177303.96 184182.01  81461.05  72055.87  38450.67  41881.58 
##       778       779       780       781       782       783       784       785 
##  42290.32  46685.39  41087.56  41407.36  41250.08  45800.94  40541.37  40239.03 
##       786       787       788       789       790       791       792       793 
##  45349.92  48372.85  46628.39  43979.27  46716.32  50081.88  50487.50  45034.48 
##       794       795       796       797       798       799       800       801 
##  41072.84  41657.45  42067.31  36703.09  36784.79  36057.87  35948.61  47543.78 
##       802       803       804       805       806       807       808       809 
##  50271.35  56879.86  59027.06  53594.62  60752.44  68481.64  57358.82  50437.11 
##       810       811       812       813       814       815       816       817 
##  44293.09  48100.81  52466.33  50638.17  38654.72  36960.28  44533.57  52878.93 
##       818       819       820       821       822       823       824       825 
##  44535.07  38922.27  32632.19  43802.66  43981.02  41387.66  35563.19  44723.41 
##       826       827       828       829       830       831       832       833 
##  52763.18  57223.73  54069.09  53399.30  55960.48  59749.74  60403.20  53711.37 
##       834       835       836       837       838       839       840       841 
##  55531.54  54950.55  57194.42  60366.40  58530.77  49768.56  55217.97  50779.74 
##       842       843       844       845       846       847       848       849 
##  44529.83  48323.33  37611.49  37178.31  49413.84  51098.05  51979.63  51617.92 
##       850       851       852       853       854 
##  55819.41  66627.04  61773.72  50654.51  49979.23 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.809
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##       original     bias    std. error
## t1*    5.27131   0.781609    3.986204
## t2* 2721.92923 163.693233  887.961302
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value  Upper CI
## 1 interrupt_var    1.195423       5.272817   13.7667
## 2    lag_depvar 1667.860082    2761.531150 4530.8501

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
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#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 8.205857 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 192.101143 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 54.522571 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 1.884286 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.000000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 26.407143 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 14.927000 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 15.711429 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.000000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.000000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 313.759429 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2808, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
    

fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2808 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-09-09 00:04:58 sería de: 26.848 pesos// Percentil 95% más alto proyectado: 35.188,75

Según TimeGPT: La proyección de la UF a 298 días más 2026-07-04 sería de: 40.201,26 pesos// Percentil 80% más alto proyectado: 42.239,44 pesos// Percentil 95% más alto proyectado: 42.556,3

Según prophet: La proyección de la UF a 298 días más 2026-07-04 sería de: 42.517 pesos// Percentil 95% más alto proyectado: 50.093

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26460.95 26321.73
Lo.80 26594.36 26486.94
Point.Forecast 26848.21 26799.01
Hi.80 31608.29 32186.25
Hi.95 34460.82 35038.08


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.4788  1028.6175
## s.e.  0.1010    41.4439
## 
## sigma^2 = 38077:  log likelihood = -521.14
## AIC=1048.28   AICc=1048.61   BIC=1055.35
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.4750   849.7566   5.4671
## s.e.  0.1014   348.7572  10.5782
## 
## sigma^2 = 38456:  log likelihood = -521.01
## AIC=1050.02   AICc=1050.57   BIC=1059.44
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 601.3384 592.9478 566.8658
Lo.80 752.5218 743.7341 659.1232
Point.Forecast 1038.1140 1028.5762 876.3411
Hi.80 1323.7061 1313.4183 1165.1444
Hi.95 1474.8895 1464.2046 1354.7715


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.6         Rcpp_1.1.0         
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.4.0        ggiraph_0.9.0      
## [10] tidytext_0.4.3      DT_0.33             janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.4         plotly_4.11.0      
## [16] xts_0.14.1          forecast_8.24.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-16          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.1.0        
## [28] tidyr_1.3.1         tibble_3.3.0        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.9.0        lattice_0.22-6     
## [34] GGally_2.3.0        ggplot2_3.5.2       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.9.1      httr_1.4.7         
## [40] readxl_1.4.5        zoo_1.8-14          stringr_1.5.1      
## [43] stringi_1.8.7       DataExplorer_0.8.4  data.table_1.17.8  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9        cellranger_1.1.0    httr2_1.2.1        
##   [4] lifecycle_1.0.4     StanHeaders_2.32.10 doParallel_1.0.17  
##   [7] globals_0.18.0      vroom_1.6.5         MASS_7.3-60.2      
##  [10] crosstalk_1.2.1     magrittr_2.0.3      sass_0.4.10        
##  [13] rmarkdown_2.29      jquerylib_0.1.4     yaml_2.3.10        
##  [16] fracdiff_1.5-3      doRNG_1.8.6.2       askpass_1.2.1      
##  [19] pkgbuild_1.4.8      DBI_1.2.3           abind_1.4-8        
##  [22] quadprog_1.5-8      nnet_7.3-19         rappdirs_0.3.3     
##  [25] sandwich_3.1-1      inline_0.3.21       data.tree_1.1.0    
##  [28] tokenizers_0.3.0    listenv_0.9.1       anytime_0.3.12     
##  [31] spatial_7.3-17      parallelly_1.45.1   codetools_0.2-20   
##  [34] xml2_1.3.8          tidyselect_1.2.1    farver_2.1.2       
##  [37] urca_1.3-4          its.analysis_1.6.0  matrixStats_1.5.0  
##  [40] stats4_4.4.0        jsonlite_2.0.0      ellipsis_0.3.2     
##  [43] Formula_1.2-5       iterators_1.0.14    systemfonts_1.2.3  
##  [46] foreach_1.5.2       tools_4.4.0         glue_1.8.0         
##  [49] xfun_0.52           TTR_0.24.4          ggfortify_0.4.19   
##  [52] loo_2.8.0           withr_3.0.2         timeSeries_4041.111
##  [55] fastmap_1.2.0       boot_1.3-30         openssl_2.3.3      
##  [58] caTools_1.18.3      digest_0.6.37       timechange_0.3.0   
##  [61] R6_2.6.1            lfe_3.1.1           colorspace_2.1-1   
##  [64] networkD3_0.4.1     gtools_3.9.5        generics_0.1.4     
##  [67] htmlwidgets_1.6.4   ggstats_0.10.0      pkgconfig_2.0.3    
##  [70] gtable_0.3.6        timeDate_4041.110   lmtest_0.9-40      
##  [73] S7_0.2.0            selectr_0.4-2       janeaustenr_1.0.0  
##  [76] htmltools_0.5.8.1   carData_3.0-5       tseries_0.10-58    
##  [79] snakecase_0.11.1    knitr_1.50          rstudioapi_0.17.1  
##  [82] tzdb_0.5.0          uuid_1.2-1          nlme_3.1-164       
##  [85] curl_6.4.0          cachem_1.1.0        KernSmooth_2.23-22 
##  [88] parallel_4.4.0      fBasics_4041.97     pillar_1.11.0      
##  [91] vctrs_0.6.5         gplots_3.2.0        slam_0.1-55        
##  [94] car_3.1-3           dbplyr_2.5.0        xtable_1.8-4       
##  [97] evaluate_1.0.4      mvtnorm_1.3-3       cli_3.6.5          
## [100] compiler_4.4.0      crayon_1.5.3        rngtools_1.5.2     
## [103] future.apply_1.20.0 labeling_0.4.3      rstan_2.32.7       
## [106] QuickJSR_1.8.0      viridisLite_0.4.2   assertthat_0.2.1   
## [109] lazyeval_0.2.2      Matrix_1.7-0        hms_1.1.3          
## [112] bit64_4.6.0-1       future_1.67.0       nixtlar_0.6.2      
## [115] extraDistr_1.10.0   igraph_2.1.4        RcppParallel_5.1.10
## [118] bslib_0.9.0         quantmod_0.4.28     bit_4.6.0
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))